LocalM™ AiD Framework
Enterprise Architecture Principles for Governing AI-Assisted Software Development
What Is This Framework?
LocalM™ AiD provides 27 Enterprise Architecture Principles for governing AI coding agents (GitHub Copilot, Cursor, Claude Code, etc.) and AI-assisted software development practices.
Scope: This framework defines EA Principles for governing AI-assisted software development—how AI coding tools are evaluated, configured, controlled, secured, and monitored. It is NOT about implementation timelines or solutions but specifically about foundational principles that guide governance decisions.
Notice: This framework is provided “AS IS” under the Mozilla Public License 2.0. Adapt it to your organization’s specific requirements.
Who Should Use This?
| Role | Use Case |
|---|---|
| Enterprise Architects | Define AI governance standards |
| Security Teams | Establish AI agent permission boundaries |
| Engineering Leaders | Implement AI coding tool policies |
| Platform Engineers | Configure MCP servers and agent environments |
| Compliance Officers | Create audit trails for AI-assisted development |
Core Tenets
These five tenets underpin all principles in the framework:
flowchart TB
subgraph TENETS["LOCALM-AID CORE TENETS"]
direction TB
T1["HUMAN AGENCY<br/>Programmer directs; AI assists"]
T2["STRUCTURED INTERACTION<br/>Methodology over vibe coding"]
T3["CONTINUOUS VALIDATION<br/>Quality gates throughout"]
T4["TRACEABILITY<br/>All interactions auditable"]
T5["PROGRESSIVE MATURITY<br/>Grow responsibly"]
T1 --- T2 --- T3
T4 --- T5
end
style TENETS fill:#f8fafc,stroke:#6366f1,stroke-width:2px
style T1 fill:#e0e7ff,stroke:#6366f1
style T2 fill:#e0e7ff,stroke:#6366f1
style T3 fill:#e0e7ff,stroke:#6366f1
style T4 fill:#e0e7ff,stroke:#6366f1
style T5 fill:#e0e7ff,stroke:#6366f1
Principle Structure
Every LocalM-AiD principle follows the TOGAF-aligned four-part structure:
| Component | Description |
|---|---|
| Name | Short, memorable identifier (e.g., DM-001) |
| Statement | Clear, declarative principle (1-2 sentences) |
| Rationale | Business/technical justification (“why”) |
| Implications | Consequences and requirements for adoption |
Plus LocalM-AiD-specific additions:
| Component | Description |
|---|---|
| Maturity Alignment | Requirements at Base, Medium, High levels |
| Governance | Exception handling, compliance measures |
| Related Principles | Cross-references within the framework |
Seven Principle Categories
flowchart TB
subgraph STRATEGY["STRATEGY & PLANNING LAYER"]
PS["PS: Planning &<br/>Strategy (4)"]
TSI["TSI: Tool Selection<br/>& Integration (3)"]
end
subgraph DEV["DEVELOPMENT LAYER"]
TTA["TTA: Team Training<br/>& Adoption (2)"]
DC["DC: Development<br/>& Coding (6)"]
end
subgraph DELIVERY["DELIVERY LAYER"]
TQC["TQC: Testing &<br/>Quality Control (3)"]
DM["DM: Deployment<br/>& Maintenance (2)"]
end
subgraph GOV["GOVERNANCE LAYER"]
GSC["GSC: Governance, Security & Compliance (2)"]
end
STRATEGY --> DEV --> DELIVERY --> GOV
style STRATEGY fill:#e3f2fd,stroke:#1976d2
style DEV fill:#e8f5e9,stroke:#388e3c
style DELIVERY fill:#fff3e0,stroke:#f57c00
style GOV fill:#fce4ec,stroke:#c2185b
Total: 22 Principles
| Category | Code | Principles | Focus Area |
|---|---|---|---|
| Planning & Strategy | PS | 4 | Strategic AI integration |
| Tool Selection & Integration | TSI | 3 | AI tool ecosystem choices |
| Team Training & Adoption | TTA | 2 | Human capability development |
| Development & Coding | DC | 6 | AI-assisted coding practices |
| Testing & Quality Control | TQC | 3 | AI quality assurance |
| Deployment & Maintenance | DM | 2 | AI operations lifecycle |
| Governance, Security & Compliance | GSC | 2 | AI risk management |
Maturity Model
flowchart TB
subgraph HIGH["HIGH (L3) - Advanced"]
H["Agentic AI | Autonomous Testing | CI/CD AI<br/>Predictive Planning | Self-Healing Code"]
end
subgraph MEDIUM["MEDIUM (L2) - Enhanced"]
M["AI Code Review | Structured Prompting<br/>Quality Gates | Documentation Generation"]
end
subgraph BASE["BASE (L1) - Foundation"]
B["Human Review | Basic Training | Code Completion<br/>Security Scanning | Traceability"]
end
BASE --> MEDIUM --> HIGH
style HIGH fill:#c8e6c9,stroke:#2e7d32
style MEDIUM fill:#fff9c4,stroke:#f9a825
style BASE fill:#e3f2fd,stroke:#1565c0
Legend: Each level builds on requirements of levels below
| Level | Name | AI Autonomy | Requirements |
|---|---|---|---|
| Base | Foundation | L1: AI-Assisted Options | Mandatory for all |
| Medium | Enhanced | L2: AI-Assisted Selection | Base + expanded controls |
| High | Advanced | L3: Partial Automation | Medium + autonomous caps |
Navigate Principles
| Category | Link | Principle Count |
|---|---|---|
| All Principles | View All → | 22 |
| Planning & Strategy | PS Principles → | 4 |
| Tool Selection & Integration | TSI Principles → | 3 |
| Team Training & Adoption | TTA Principles → | 2 |
| Development & Coding | DC Principles → | 6 |
| Testing & Quality Control | TQC Principles → | 3 |
| Deployment & Maintenance | DM Principles → | 2 |
| Governance, Security & Compliance | GSC Principles → | 2 |
Standards Alignment
LocalM-AiD principles align with established EA and AI development standards:
| Standard | Alignment |
|---|---|
| TOGAF | Principle structure (Name, Statement, Rationale, Implications) |
| AGENTS.md | Agent configuration principles (DC-005) |
| SKILL.md | Capability definition standards |
| MCP | Model Context Protocol integration (TSI) |
| A2A | Agent-to-Agent coordination (TSI) |